Query Generation System with Implicit Join Resolution
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Solution Overview
Problem
Existing data processing solutions fail to optimally integrate query generation, data store management, and query processing components, leading to inefficiencies in handling both batch and streaming data, particularly due to complex query languages and the need for explicit join relationships, which results in suboptimal performance and increased computational resources.
Innovation Solution
A data platform that includes a query generation component to simplify query creation by omitting join qualifications, a data ingress component for data acquisition, and a query processing component that utilizes a relational data store to execute queries efficiently, with implicit join determination based on the data schema, and a data store management component for optimized data storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex query languages with explicit join relationships are used, then query precision and data relationship accuracy are improved, but query creation complexity and computational resource requirements increase
Solution Approach 1:
The system introduces an intermediary component that automatically generates and manages join relationships between data sources. This intermediary translates high-level user queries into detailed join specifications without requiring users to manually define them, thereby maintaining query precision while reducing creation complexity
Solution Approach 2:
The system enables self-service by allowing the query processing component to automatically determine and execute join relationships based on schema information. The system serves itself by autonomously resolving data relationships without human intervention, improving both precision and ease of use
2Productivity
If integrated query generation and processing components are used, then query execution efficiency is improved, but system complexity increases
Solution Approach 1:
The system merges the query generation component and query processing component into a unified integrated system. This combination eliminates the need for separate, independently managed components, reducing overall system complexity while maintaining high execution efficiency through coordinated operation
Solution Approach 2:
The integrated system performs multiple functions including query generation, join relationship determination, and query execution within a single unified framework. This multi-functionality reduces the number of separate systems needed, simplifying the overall architecture while improving execution efficiency
3Ease of operation
If implicit join determination based on data schema is used, then query creation ease is improved, but potential for join errors increases
Solution Approach 1:
The system implements feedback mechanisms where the query processing component validates implicit join relationships against the data schema and provides corrections or warnings. This feedback loop ensures that automatically determined joins are accurate, maintaining reliability while preserving ease of query creation
Solution Approach 2:
The system performs preliminary validation of join relationships by analyzing the data schema before executing queries. By pre-checking and pre-validating join paths, the system ensures accuracy is maintained while allowing users to create queries easily without manual join specifications
Data Source
AI summary
A query generation and processing system includes a relational data store, a query generator, and a query processor. The relational data store stores data ingested from data sources in a first and second datasets. The query generator interprets a data expression in a simplified query language to generate a query in a structured query language based on identifying quads corresponding to the first and second datasets in the data expression and determining an implicit join between the quads based on an unambiguous relationship obtainable from a schema of the first and datasets, in which the data expression does not expressly identify a join between the first quad and the second quad. The query processor generates a query pipeline that uses the data of the first and second datasets stored by the relational data store to execute the query generated by the query processor.